Maine Prisoner Advocacy Coalition Project 2

0.1 Executive Summary

This project was completed in collaboration with Jan Collins, the Assistant Director of the Maine Prisoner Advocacy Coalition and with guidance from Bates Professors Michael Rocque from the Sociology Department and M. Mena from the Anthropology Department. The focus of this project was to visualize prison and parole data to aid in the ongoing efforts of the Maine Prisoner Advocacy Coalition to campaign for the reinstatement of parole in Maine. This report includes animated visualizations and interactive maps to inform the work of the Maine Prisoner Advocacy Coalition.

Quick Issue Background

Methodology Overview

Recommendations/Next Steps

Summary/Conclusion

0.2 List of Figures

0.3 Introduction

0.3.1 Background and Context

Issue Background/Statistics/Definitions

Work of MPAC

0.3.2 Community Partner Identified Problem

What We Did

We hope that this report will complement the work of the Maine Prisoner Advocacy Coalition by visualizing existing criminal justice data to inform efforts to reinstate parole in Maine.

0.4 Methodological Approach

0.4.1 Depth and Accessibility of Data

0.4.2 Our Approach

After realizing the discrepancies in the Vera data set, our team collected information from multiple sources. This information was tied together in two different data sets; a set that looks at prison statistics on a county level and a set on the state level. Our data knit together information from the Vera Institute, Urban Institute and the Bureau of Justice Statistics. This data was then utilized to understand the current state of the prison system in Maine and to build two interactive maps of the United States for further research. Through these visualizations of data our team aimed to aid in the understanding of how Maine will be impacted by the reimplementation of parole.

0.5 Results and Discussion

0.5.1 Maine State Analysis

0.5.2 New England Region Analysis

0.5.3 Measuring the Effectiveness of Parole

0.5.4 Nationwide Tool for Further Data Exploration

The data collected has been compiled into two separate interactive national maps that portray the information on a county and state level. The state map shows the number of adults on parole per one hundred thousand adult residents. This information is useful in showing which states have the most wide reaching parole and which states, like Maine, have not parole in recent years. It is worth noting that while Maine has had no parole recently, there are still a few individuals who were eligible for parole due to their sentences coming before parole was removed. The county level map displays the ratio of the jail population to the total population per one thousand residents. This normalizes the differences in population size and starts to look at which regions have higher and longer rates of incarcerations. Both of these maps will aid in further understanding the state of Maine prison reform compared to the rest of the US.

incar <- read.csv("incarceration_trends.csv")
#Importing state/County JSON containing fip and geometry of the counties
all_counties <- geojsonio:: geojson_read(x= "https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json", what= "sp")

## Registered S3 method overwritten by 'geojsonsf':
##   method        from   
##   print.geojson geojson

#AK<-geojsonio::geojson_read(x = "https://raw.githubusercontent.com/billcccheng/us-county-boundary-api/master/states/AK.json", what = "sp")
by_year<-incar$year
maine_data <- by_year =="1970"
year_1970 <- incar[maine_data,]

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.5     ✔ purrr   0.3.4
## ✔ tibble  3.1.6     ✔ stringr 1.4.0
## ✔ tidyr   1.2.0     ✔ forcats 0.5.1
## ✔ readr   2.1.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::as.difftime() masks base::as.difftime()
## ✖ lubridate::date()        masks base::date()
## ✖ dplyr::filter()          masks stats::filter()
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## ✖ dplyr::lag()             masks stats::lag()
## ✖ lubridate::setdiff()     masks base::setdiff()
## ✖ lubridate::union()       masks base::union()

library(tidymodels)

## ── Attaching packages ────────────────────────────────────── tidymodels 0.2.0 ──

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## ✔ parsnip      0.2.1      ✔ yardstick    0.0.9 
## ✔ recipes      0.2.0

## ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
## ✖ scales::discard() masks purrr::discard()
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## • Dig deeper into tidy modeling with R at https://www.tmwr.org

library(knitr)
library(xaringanthemer)
library(maps)

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

library(ggplot2)
library(tidyverse)
library(corrplot)

## corrplot 0.92 loaded

library(skimr)
library(robotstxt)
library(tidyr) 
library(leaflet) ## For leaflet interactive maps
library(sf) ## For spatial data

## Linking to GEOS 3.9.1, GDAL 3.4.0, PROJ 8.1.1; sf_use_s2() is TRUE

library(RColorBrewer) ## For colour palettes
library(htmltools) ## For html
library(leafsync) ## For placing plots side by side
library(kableExtra) ## Table output

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
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library(geojsonio)

## 
## Attaching package: 'geojsonio'

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year_1970<- year_1970 %>%
  mutate(county_name = str_to_lower(str_replace_all(county_name, pattern = " County", replacement = "")))

all_counties <- st_as_sf(all_counties)
all_counties <- all_counties %>%
  mutate(NAME = str_to_lower(NAME))

joint_info_map <- year_1970 %>%
  left_join(all_counties, by = c("county_name" = "NAME")) %>%
  st_as_sf()


joint_info_map  <- joint_info_map  %>%
  #filter(total_pop) %>%
  mutate(total_pop = total_pop)
#this is for changing color 
bins <- c(0, 0.5,1,2,4,15)
pal <- colorBin("Blues", domain = joint_info_map$total_jail_pop/(joint_info_map$total_pop/1000), bins = bins)

labels <- sprintf("<strong>%s</strong><br/>%g Total Jail Pop/Total Pop",
                 joint_info_map$county_name, joint_info_map$total_jail_pop/(joint_info_map$total_pop/1000)) %>% lapply(htmltools::HTML)

joint_info_map%>%
leaflet() %>%
  addTiles()%>%
  setView(lng = -80,
          lat = 34.5,
          zoom = 4) %>%
  addPolygons(fillColor = ~pal(total_jail_pop/(total_pop/1000)),
              fillOpacity = 1,
              color = "black",
              opacity = 1,
              weight = 0.5,
              highlight = highlightOptions(
                weight = 3,
                color = "blue",
                fillOpacity = .2,
                bringToFront = TRUE),
              label = labels) %>%
  addLegend(
    position = "topright",
    pal = pal,
    values = ~total_jail_pop/(total_pop/1000),
    title = "Ratio of Jail Pop per 1000",
    opacity = 1)

## Warning in pal(total_jail_pop/(total_pop/1000)): Some values were outside the
## color scale and will be treated as NA

0.6 Recommendations for Next Steps

There is a lot of work still to be done with this research. Using the national maps alongside the Maine specific visualizations, it is important to identify which states have a similar make up of prison systems and how those states have dealt with parole. This is in service of understanding how reintroducing parole in Maine will impact the system and how many people may be eligible. This is a roundabout way of reaching these conclusions due to the lack of reliable information.

0.6.1 Data Collection

This leads to the next recommendation which would be to lobby for more thorough data collection which focuses on parole eligibility, recidivism and costs related to parole. Without these more direct data points collected on an individual basis, any understanding of the impacts of the proposed Maine parole system will be loose approximations. There are obvious concerns with data privacy regarding and individual’s data but HIPA is generally satisfied if the data is anonymous. Through more research and effective data collection/transparency, MPAC will be able to build a strong story of how parole will look in Maine.